Incremental Sparsification for Real-time Online Model Learning

Nguyen-Tuong, D; Peters, J

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Incremental Sparsification for Real-time Online Model Learning

Nguyen-Tuong, D., & Peters, J. (2010). Incremental Sparsification for Real-time Online Model Learning. Cambridge, MA, USA: JMLR.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C042-2 Version Permalink: https://hdl.handle.net/21.11116/0000-0002-81F1-B

Genre: Conference Paper

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http://proceedings.mlr.press/v9/nguyen_tuong10a/nguyen_tuong10a.pdf (Publisher version) Open Access status unknown

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Creators:
Nguyen-Tuong, D^{1, 2}, Author
Peters, J^{1, 2}, Author

Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794

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Abstract: Online model learning in real-time is required by many applications such as in robot tracking control. It poses a difficult problem, as
fast and incremental online regression with
large data sets is the essential component
which cannot be achieved by straightforward
usage of off-the-shelf machine learning methods
(such as Gaussian process regression or
support vector regression). In this paper,
we propose a framework for online, incremental
sparsification with a fixed budget designed
for large scale real-time model learning.
The proposed approach combines a
sparsification method based on an independence
measure with a large scale database.
In combination with an incremental learning
approach such as sequential support vector
regression, we obtain a regression method
which is applicable in real-time online learning.
It exhibits competitive learning accuracy
when compared with standard regression
techniques. Implementation on a real
robot emphasizes the applicability of the proposed
approach in real-time online model
learning for real world systems.

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Dates: Date issued: 2010-05

Publication Status: Issued

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Identifiers: BibTex Citekey: 6505

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Title: Thirteenth International Conference on Artificial Intelligence and Statistics (AI & Statistics 2010)

Place of Event: Chia Laguna Resort, Italy

Start-/End Date: 2010-05-13 - 2010-05-15

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Title: JMLR Workshop and Conference Proceedings

Source Genre: Series

Creator(s):
Teh, YW, Editor
Titterington, M, Editor

Affiliations:
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Publ. Info: Cambridge, MA, USA : JMLR

Pages: - Volume / Issue: 9 Sequence Number: - Start / End Page: 557 - 564 Identifier: -